_base_ = [
    "../datasets/coco_detection.py",
    "../_base_/schedules/schedule_1x.py",
    "../_base_/default_runtime.py",
]

img_scale = (640, 640)  # height, width

model = dict(
    type="CenterNet",
    backbone=dict(
        type="ResNet",
        depth=18,
        norm_eval=False,
        norm_cfg=dict(type="BN"),
        init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet18"),
    ),
    neck=dict(
        type="CTResNetNeck",
        in_channel=512,
        num_deconv_filters=(256, 128, 64),
        num_deconv_kernels=(4, 4, 4),
        use_dcn=True,
    ),
    bbox_head=dict(
        type="CenterNetHead",
        num_classes=80,
        in_channel=64,
        feat_channel=64,
        loss_center_heatmap=dict(type="GaussianFocalLoss", loss_weight=1.0),
        loss_wh=dict(type="L1Loss", loss_weight=0.1),
        loss_offset=dict(type="L1Loss", loss_weight=1.0),
    ),
    train_cfg=None,
    test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100),
)

# We fixed the incorrect img_norm_cfg problem in the source code.
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)

train_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True, color_type="color"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="PhotoMetricDistortion",
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18,
    ),
    dict(
        type="RandomCenterCropPad",
        crop_size=(512, 512),
        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
        mean=[0, 0, 0],
        std=[1, 1, 1],
        to_rgb=True,
        test_pad_mode=None,
    ),
    dict(type="Resize", img_scale=(512, 512), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]

test_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True),
    dict(
        type="MultiScaleFlipAug",
        img_scale=img_scale,
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(
                type="RandomCenterCropPad",
                ratios=None,
                border=None,
                mean=[0, 0, 0],
                std=[1, 1, 1],
                to_rgb=True,
                test_mode=True,
                test_pad_mode=["logical_or", 31],
                test_pad_add_pix=1,
            ),
            dict(type="Pad", pad_to_square=True, pad_val=dict(img=(0, 0, 0))),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="DefaultFormatBundle"),
            dict(
                type="Collect",
                meta_keys=(
                    "filename",
                    "ori_filename",
                    "ori_shape",
                    "img_shape",
                    "pad_shape",
                    "scale_factor",
                    "flip",
                    "flip_direction",
                    "img_norm_cfg",
                    "border",
                ),
                keys=["img"],
            ),
        ],
    ),
]

dataset_type = "CocoDataset"
data_root = "data/coco/"

# Use RepeatDataset to speed up training
data = dict(
    samples_per_gpu=16,
    workers_per_gpu=4,
    train=dict(
        _delete_=True,
        type="RepeatDataset",
        times=5,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root + "annotations/instances_train2017.json",
            img_prefix=data_root + "train2017/",
            pipeline=train_pipeline,
        ),
    ),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)

# optimizer
# Based on the default settings of modern detectors, the SGD effect is better
# than the Adam in the source code, so we use SGD default settings and
# if you use adam+lr5e-4, the map is 29.1.
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))


# learning policy
# Based on the default settings of modern detectors, we added warmup settings.
lr_config = dict(
    policy="step",
    warmup="linear",
    warmup_iters=1000,
    warmup_ratio=1.0 / 1000,
    step=[18, 24],
)  # the real step is [18*5, 24*5]
runner = dict(max_epochs=28)  # the real epoch is 28*5=140


# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (16 samples per GPU)
auto_scale_lr = dict(base_batch_size=128)
